41 results about "State predictor" patented technology

A mobile device and method for predictive computing of variable mobile link parameters per session and state in a future time interval within Radio Access Networks (RAN). The system narrows its prediction errors as time progresses. An ideal control value is also estimated, so that mobile QoS applications can determine their intervention point while coexisting with link layer resource management mechanisms. The system is provided with certain measurement elements from the RAN. With the information contained in these elements, the system estimates the Received Signal Strength (RSSI), Received Wideband Power (RSCP), Signal Interference Ratio (SIR), Bit Error Rate (BER), the transmission delay per frame (delay), the variation between delay measurements throughout a certain number of measurement time frames and the mean bit throughput rate (Chip Rate) for a future time interval. The system also calculates the optimal power control (gain) for a defined value target of a given link parameter. The estimates are passed on to other Systems for further processing.

A control system and control method provides improved control of various loads by selecting the switch configuration of an associated power circuit having N binary switches, based in part on a finite state machine. The control system includes an embedded simulator, a present state contemplator and a next state contemplator to predict the operation of the load based on various switch configurations of the power circuit. The various switch states of the power circuit are modeled by the finite state machine (having up to 2^N switch states) such that at any time, the power circuit switches are in a Present State and there are a plurality of Next States which are one or more switch transitions away from the Present State. The embedded simulator estimates the operating conditions of the load based on measured operational characteristics and the Present State. The present state contemplator determines, based on the operating conditions, whether a switch state transition should be contemplated. If so, then the next state contemplator determines the optimal next state based on a set of performance criterion and sends a state switch command to the power circuit.

A target tracking arrangement predicts the state of a target. The predictor may be a Kalman filter. In the presence of a target which is maneuvering, the prediction may be in error. A maneuver detector is coupled to receive residuals representing the difference between the predictions and the target state. The maneuver detector is matched to the predictor or Kalman filter to thereby tend to reduce the undesirable effects of system noise. The matching may be of the frequency response.

A predication control based multi-time-lag intelligent power grid economic scheduling method relates to intelligent power grid scheduling. The method includes 1, constructing a network structure typological graph and obtaining an adjacent matrix, a degree matrix and a Laplacian matrix; 2, constructing a mathematic model for intelligent power grid economic scheduling and acquiring system parameters; 3, designing a power generation device output state predicator; 4, giving initial values of the power generation devices; 5, setting a communication mode so as to enable each power generation device to communicate with an adjacent device only, operating a distributive control algorithm and adjusting the state of each node; 6, judging whether the incremental cost state of all the nodes achieves consistence or not, if the consistence is achieved, turning to step 7, and if the consistence is not achieved, returning to step 5; 7, obtaining the optimal solution of the intelligent power grid economic scheduling problem.

The invention provides an L1 adaptive ship power positioning double-loop control system based on a neural network. The control system comprises a marine ship, an outer ring position tracking loop controller, an L1 adaptive inner loop controller based on a neural network and a path generator. The outer ring position tracking loop controller can track a reference path of the system to obtain a virtual speed instruction; the L1 adaptive inner loop controller based on a neural network comprises an adaptive wavelet neural network approaching device, a state predictor, a parameter adaptive law and an L1 control law; the L1 control law comprises a filter; and the L1 adaptive inner loop controller based on a neural network solves the problem of uncertainty in the system, and approaches a coupling term in the system using a recursive wavelet neural network. The L1 controller introduces a low-pass filter into the feedback loop during feedback, thereby weakening the influence of unknown high-frequency noise in control signals on high-frequency noise. The control system can be applied to control of other ship power positioning systems.

A code-enhanced, multi-user detection, Reduced Algorithm Receiver in a multiuser communication system reduces the signal processing requirements by limiting the number of searches between consecutive symbol cycles using information from an encoder state diagram. The Receiver makes use of a convolutional encoder's state machine properties to mark possible transitions as invalid based on the current state of the multiple users'encoders, thus removing them from the search tree. The Reduced Algorithm Receiver comprises a Signal Sampler for converting a single composite waveform signal (Rx) to digitized composite signal (DRx), a Parameter Estimator for determining waveform characteristics of each of the multiple users, a Memory for storing predetermined training sequences for each of the users, a Next State Predictor that reduces the amount of symbols being checked during a current cycle, a Waveform Approximator that receives the user waveform characteristics data and a set of possible bits from the next state predictor and attempts to simulate modulation of the individual user signal, a Demodulator for comparing the set of possible composite waveforms (~Tx) from the Waveforms Approximator to the digitized composite waveforms (DRx) and determining the best match waveform, and a Convolutional Decoder for receiving the best match symbols and decoding them for each of the users.

The invention belongs to the technical field of rotating machine state monitoring and fault diagnosis, and discloses a rotating machine fault information processing method, processing system, processing terminal and a medium, and the method comprises the steps: constructing a neural network model comprising a depth feature extractor, a domain classifier and a state predictor; automatically extracting migration fault features from laboratory simulation data and rotating part monitoring data in actual engineering equipment by using a depth feature extractor through a neural network model; utilizing a domain classifier to shorten the difference between two kinds of data distribution, utilizing a state predictor, introducing domain adaptation constraints, forming a fault diagnosis model based on a depth domain adaptive adversarial network, and utilizing the model to realize intelligent fault diagnosis of the rotating machinery. According to the method, migration fault features in laboratory simulation data and actual engineering data can be accurately extracted, a fault migration diagnosis model capable of being applied to the rotating part is formed, and an ideal effect is achieved through actual case utilization.

The present invention provides a controller for controlling a modeled plant robustly against disturbance. The controller comprises an estimator and a control unit. The estimator estimates disturbance applied to the plant. The control unit determines an input to the plant so that an output of the plant converges to a desired value. The input to the plant is determined to include a value obtained by multiplying the estimated disturbance by a predetermined gain. Since estimated disturbance is reflected in the input to the plant, control having robustness against disturbance is implemented. The controller may comprise a state predictor. The state predictor predicts the output of the plant based on the estimated disturbance and dead time included in the plant. The control unit determined the input to the plant so that the predicted output converges to a desired value. Since the state predictor allows for the dead time, the accuracy of the control is improved. The estimated disturbance is reflected in the predicted output, an error between the predicted output and an actual output of the plant is removed.

The invention provides an under-actuated UUV horizontal plane trajectory tracking control method based on state prediction compensation. The under-actuated UUV horizontal plane trajectory tracking control method comprises the steps of: step 1, acquiring position and attitude information according to a current task by means of a UUV; step 2, acquiring position and attitude error variables under motion coordinates by utilizing a mathematical model of the under-actuated UUV; 3, calculating a virtual control law by stabilizing the position and attitude error variables; step 4, constructing a position and attitude state predictor according to a kinematics model of the UUV, and constructing a speed state predictor according to a dynamics model of the UUV; step 5, and constructing an actual trajectory tracking control law of the UUV according to predicted state information. Under the condition that time delay exists in an under-actuated UUV executing mechanism, the UUV under the action of a UUV trajectory tracking controller designed through adopting the method can realize precise tracking of the plane trajectory, and the designed controller better meets the actual engineering requirements.

The invention provides a quantitative control method for denial of service attack and disturbance. The method comprises the following steps: selecting different updating modes for a state predictor according to whether each transmission channel is at a transmission moment and whether the transmission channel is subjected to denial of service attack at the transmission moment; the input of the to-be-stabilized system is obtained again according to the updated state predictor, and then quantitative control over the to-be-stabilized system is achieved according to the input obtained again. According to the switchable quantization controller designed by the invention, the stabilization control of the system under the conditions that the state quantity is disturbed, the bandwidth of an output channel is limited and the DoS attack exists is realized for the first time. Therefore, even if a disturbed linear system can still keep a normal operation state under the condition that the occurrence frequency and duration of the denial of service attack are limited, the system can be operated normally.

The invention relates to a novel variant patrol bomb flight system control method. The method comprises steps of firstly, researching a modeling process of a variant patrol bomb, analyzing a change rule of main aerodynamic parameters of the variant patrol bomb under different speeds and sweepback angle motion states, and obtaining a linear state space model of the variant patrol bomb containing deformation parameters. The method is advantaged in that flight control law design of the variant patrol bomb based on L1 self-adaptive control is studied, and a state predictor and a low-pass filter are designed for external disturbance and system control input high-frequency oscillation so that the control effect of the variant patrol bomb is effectively improved.

The invention is applicable to the technical field of adaptive control, and provides an L1 adaptive control system and method applicable to an integral feedback augmentation system so as to solve a technical problem that standard L1 adaptive control cannot be applied to an error integral feedback augmentation system. The L1 adaptive control system comprises a state predictor, an adaptive law and acontrol law. By using the L1 adaptive control system and method suitable for the error integral feedback augmentation system, on the basis that original characteristics of standard L1 self-adaptive control are reserved, the state predictor and the control law are changed so that the changed L1 self-adaptive control system and method can be suitable for a dynamic system with an error integral feedback augmentation state.

The invention discloses an environment art mower battery pack management system and a control method. The environment art mower battery pack management system comprises a detection module, a system controller, a motor driving module, a fault switch group, a system power supply, and a human-computer interface. According to the system controller, through the battery monomer voltage, the battery pack voltage and current obtained by the detection module, by combining with fault information fed back by the fault controller, the fault information is judged and generated according to battery state information, battery monomer voltage and the like, so that corresponding switches in the fault switch group are switched on, two motor drivers in the motor driving module are controlled, and the fault information is fed back to a battery state predictor. Meanwhile, the invention further provides a control method of the environment art mower battery pack management system. Fault judgment can be carried out on one or more battery monomers at the same time, the corresponding fault battery monomers are actively cut off, continuous power supply of the battery pack is guaranteed, and frequent replacement of the fault battery monomers is not needed.

The invention provides an operation method of an intelligent control and monitoring system for a machining process. A standard sequence value formed by each instant power obtained in the learning process serves as reference power, when a machine tool machines a workpiece, a power collector obtains each instant power value of the machine tool, and the instant power value and the reference power value serve as input of an adaptive control system, a state observer predicts a power value in the next step, a deviation tending to be stable of a next instant system is calculated by means of a self-adaptive law and reference power of the next step, and finally a gain value of automatic intervention needed by the machine tool is obtained by a control law. Thus, the power of the machine tool is automatically controlled, a cutter is protected, and the machining efficiency is improved.

The present invention provides a vehicle control device, a vehicle control method, and a storage medium capable of more appropriately performing update of software. A vehicle control device includes acontroller with embedded software which controls at least some of devices mounted in the vehicle, a communicator configured to communicate with an external device, an update controller which updatesthe software embedded in the controller according to communication with the external device by the communicator, a power supply configured to supply power to the controller, and a vehicle state predictor configured to predict a state of the vehicle, in which the update controller interrupts an update of the software when the vehicle state predictor predicts that a connection between the power supply and the controller is in a disconnected state during the update of the software.

A hard disk drive with a circuit that provides a control signal to a voice coil motor to move a head across a disk. The disk drive includes a circuit that provides a control signal to the voice coil motor to move the head. The circuit also has a state predictor and an inversion filter that compute a vibration estimate used to detect a vibration in the drive. When a threshold vibration is detected a vibration estimate is combined with the control signal to compensate for the vibration. The circuit detects the vibration without the need for an accelerometer or other sensor.

The invention discloses a multi-machine power system adaptive dynamic surface controller based on composite learning and DOB. The controller is realized based on the following steps: 1) constructing amathematical model of a large-scale power system with an SVC device; 2) designing a fuzzy logic system approximator of the multi-machine power system to approximate to obtain an unknown function in the multi-machine power system model in the step 1); and 3) designing a state predictor, adding a prediction error in the update law design, introducing a compensation signal, and combining the disturbance observer with the fuzzy logic system to design the multi-machine power system adaptive fuzzy dynamic surface controller based on composite learning and the disturbance observer. According to theinvention, a prediction error is added into the updating law design of the controller; a disturbance observer and a fuzzy logic system are combined to estimate generalized disturbance of the system, so that the approximation precision is improved, a compensation signal is introduced into the design of a control law, and a filtering error generated by using a filter in a dynamic surface method is eliminated.

The invention provides a traffic control system used for area control and a management method thereof. The system comprises a traffic central processing module, an area control module and a traffic indication module, wherein the traffic central processing module includes a traffic state predictor and a central time synchronizer; the area control module includes a traffic state calculator and a dynamic adjusting processor; the traffic indication module includes a wireless signal transceiver, a signal adjusting processor and a pulse data collector; data information collected by the pulse data collector is processed by the signal adjusting processor and then is sent to the traffic central processing module; and the central time synchronizer automatically synchronizes signal periods according to a prediction result acquired by the traffic state predictor. In the invention, manual analysis time can be saved and efficiency is increased; information feedback is rapid and traffic state sensitivity is further increased; and through integrating collected traffic information, a traffic jam condition can be greatly mitigated.

The invention discloses research of a networked control system with time delay and data packet loss. The invention provides a novel controller design based on prediction aiming at the conditions of random data packet loss and time delay in a transmission channel of a networked control system. According to the research, a sequence of data packet loss is simulated as a Bernoulli process, and is compensated by a zero-order holder (ZOH)-based module; a state predictor is used for obtaining the prediction state of a delay time; in consideration of a packet loss compensator and the state predictor, a novel correction model prediction controller is designed and provided; then, a logic programming method is adopted, and all possible conditions are brought into a prediction range; and a cost function is reformed to a synchronous least linear matrix inequality (LMI) with constraints, so that a toolbox YALMIP is employed to ultimately address this minimization of programming problem.